A semi-supervised CNN learning method with pseudo-class labels for vascular calcification detection on low dose CT scans

Jiamin Liu; Jianhua Yao; Mohammadhadi Bagheri; Ronald M. Summers

doi:10.1117/12.2513228

13 March 2019 A semi-supervised CNN learning method with pseudo-class labels for vascular calcification detection on low dose CT scans

Jiamin Liu, Jianhua Yao, Mohammadhadi Bagheri, Ronald M. Summers

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109501L (2019) https://doi.org/10.1117/12.2513228
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The recent rapid success of deep convolutional neural networks (CNN) on many computer vision tasks largely benefits from the well-annotated Pascal VOC, ImageNet, and MS COCO datasets. However, it is challenging to get ImageNetlike annotations (1000 classes) in the medical imaging domain due to the lack of clinical training in the lay crowdsourcing community. We address this problem by presenting a semi-supervised training method for neural networks with true-class and pseudo-class (un-annotated class) labels on partially annotated training data. The true-class labels are supervised annotations from clinical professionals. The pseudo-class labels are unsupervised clustering of unannotated data. Our method rests upon the hypothesis of better coherent annotations with discriminative classes leading to better trained CNN models. We validated our method on extra-coronary calcification detection in low dose CT scans. The CNN trained with true-class and 10 pseudo-classes achieved a 78.0% sensitivity at 10 false positives per scan (0.3 false positive per slice), which significantly outperformed the CNN trained with true-class only (sensitivity=25.0% at 10 false positives per patient).

Conference Presentation

Citation Download Citation

Jiamin Liu, Jianhua Yao, Mohammadhadi Bagheri, and Ronald M. Summers "A semi-supervised CNN learning method with pseudo-class labels for vascular calcification detection on low dose CT scans", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501L (13 March 2019); https://doi.org/10.1117/12.2513228

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